Perivascular adipose tissue as a relevant fat depot for cardiovascular risk in obesity

Obesity is associated with increased risk of premature death, morbidity, and mortality from several cardiovascular diseases (CVDs), including stroke, coronary heart disease (CHD), myocardial infarction, and congestive heart failure. However, this is not a straightforward relationship. Although several studies have substantiated that obesity confers an independent and additive risk of all-cause and cardiovascular death, there is significant variability in these associations, with some lean individuals developing diseases and others remaining healthy despite severe obesity, the so-called metabolically healthy obese. Part of this variability has been attributed to the heterogeneity in both the distribution of body fat and the intrinsic properties of adipose tissue depots, including developmental origin, adipogenic and proliferative capacity, glucose and lipid metabolism, hormonal control, thermogenic ability, and vascularization. In obesity, these depot-specific differences translate into specific fat distribution patterns, which are closely associated with differential cardiometabolic risks. The adventitial fat layer, also known as perivascular adipose tissue (PVAT), is of major importance. Similar to the visceral adipose tissue, PVAT has a pathophysiological role in CVDs. PVAT influences vascular homeostasis by releasing numerous vasoactive factors, cytokines, and adipokines, which can readily target the underlying smooth muscle cell layers, regulating the vascular tone, distribution of blood flow, as well as angiogenesis, inflammatory processes, and redox status. In this review, we summarize the current knowledge and discuss the role of PVAT within the scope of adipose tissue as a major contributing factor to obesity-associated cardiovascular risk. Relevant clinical studies documenting the relationship between PVAT dysfunction and CVD with a focus on potential mechanisms by which PVAT contributes to obesity-related CVDs are pointed out

Transcontextual model of motivation in the preaching of healthy lifestyles

El presente trabajo examinó la aplicación del Modelo Transcontextual de la Motivación en la predicción de estilos de vida saludables de atletas veteranos. Se utilizó una muestra de 682 atletas veteranos portugueses de ambos géneros, de edades comprendidas entre los 30 y los 76 años (M=43.64; DT=8.25), dónde a través de cuestionarios se ha medido: la satisfacción de las necesidades psicológicas básicas, la motivación, las variables del comportamiento planeado y los estilos de vida saludables. De las conclusiones alcanzadas en este trabajo, son de destacar la relevancia de fomentar la necesidad psicológica básica de relación social, ya que ésta favorecerá la motivación intrínseca, promoviendo un mayor control del comportamiento sobre las intenciones de los practicantes, generando así mejores hábitos alimenticios, hábitos de descanso y menor consumo de tabacoThe present paper has examined the application of the Transcontextual Model of motivation in the prediction of healthy lifestyles of veteran athletes. A sample of 682 Portuguese veteran athletes of both sexes, aged between 30 and 76 years (M = 43.64; SD = 8.25), were administered the following questionnaires: satisfaction of needs basic psychological, self-determination motivation, planned behavioral variables and healthy lifestyles. From the conclusions reached in this work, it is important to emphasize the importance of fostering the basic psychological need of relatedness, since this will favor the intrinsic motivation, promoting greater control of behavior over the intentions of practitioners, thus generating more healthy eating habits, rest habits and lower tobacco consumptio

Validation of the questionnaire of the transtheoretical model of change of physical exercise

El objetivo de este estudio fue traducir y validar al español el Cuestionario del Modelo Transteórico del Cambio de Ejercicio Físico de Prochaska y DiClemente (1983). Se utilizó una muestra de 812 personas, entre los 14 y los 88 años (29,5±21,7). Se realizó un análisis factorial confirmatorio, análisis de consistencia interna y validez predictiva. Los resultados del Cuestionario del Modelo Transteórico del Cambio de Ejercicio Físico presentaron valores adecuados (χ2/g.l = 4,3, CFI = 0,92, IFI = 0,92, TLI = 0,90, RMSEA = 0,06, SRMR = 0,05). La consistencia interna mostró valores encima de .70. Se halló una predicción positiva y significativa de los estadios más activos del Modelo Transteórico sobre la intención de ser físicamente activo. Este estudio ha permitido proporcionar un cuestionario válido y fiable para evaluar el estadio en el que se encuentran las personas con respecto a la práctica de ejercicio físico, en el ámbito españolThe aim of this study was to translate and validate in Spanish the Questionnaire of the Transtheoretical Model of Change of Physical Exercise, of Prochaska and DiClemente (1983), and also to make adaptations and modifications as needed. The sample was composed of 812 people, aged between 14 and 88 years (29.5+21.7). Confirmative factorial analysis, analysis of internal consistency and of predictive validity were carried out. After the confirmative factorial analysis, the Questionnaire of the Theoretical Model of Change of Physical Exercise showed acceptable results (x2/g.1=4,3, CFI=0,92, IFI= 0.92, TLI = 0.90, RMSEA = 0.06, SRMR = 0,05). Similarly, internal consistency obtained from the respective dimensions showed values above .70. A positive and significant prediction of the most active stages of the Transtheoretical Model (action and maintenance) was found on the ‘intention to be physically active’. This study has enabled the provision of a valid and reliable questionnair

Black-Box α-divergence minimization

Black-box alpha (BB-α) is a new approximate inference method based on the minimization of α-divergences. BB-α scales to large datasets because it can be implemented using stochastic gradient descent. BB-α can be applied to complex probabilistic models with little effort since it only requires as input the likelihood function and its gradients. These gradients can be easily obtained using automatic differentiation. By changing the divergence parameter α, the method is able to interpolate between variational Bayes (VB) (α → 0) and an algorithm similar to expectation propagation (EP) (α = 1). Experiments on probit regression and neural network regression and classification problems show that BB-a with non-standard settings of α, such as α = 0.5, usually produces better predictions than with α → 0 (VB) or α = 1 (EP).JMHL acknowledges support from the Rafael del Pino Foundation. YL thanks the Schlumberger Foundation Faculty for the Future fellowship on supporting her PhD study. MR acknowledges support from UK Engineering and Physical Sciences Research Council (EPSRC) grant EP/L016516/1 for the University of Cambridge Centre for Doctoral Training, the Cambridge Centre for Analysis. TDB thanks Google for funding his European Doctoral Fellowship. DHL acknowledge support from Plan National I+D+i, Grant TIN2013-42351-P and TIN2015- 70308-REDT, and from Comunidad de Madrid, Grant S2013/ICE-2845 CASI-CAM-CM. RET thanks EPSRC grant #EP/L000776/1 and #EP/M026957/1

Composição centesimal da castanha-do-brasil (Bertholletia excelsa) comercializada em Macapá e Santana (AP).

O objetivo deste trabalho foi avaliar o valor nutritivo através da composição centesimal da amêndoa da castanha-do-brasil comercializada nas quatro principais feiras dos municípios de Macapá e Santana oriundas das reservas de Iratapuru, Cajari e Maracá

On the impact of covariance functions in multi-objective Bayesian optimization for engineering design

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordMulti-objective Bayesian optimization (BO) is a highly useful class of methods that can effectively solve computationally expensive engineering design optimization problems with multiple objectives. However, the impact of covariance function, which is an important part of multi-objective BO, is rarely studied in the context of engineering optimization. We aim to shed light on this issue by performing numerical experiments on engineering design optimization problems, primarily low-fidelity problems so that we are able to statistically evaluate the performance of BO methods with various covariance functions. In this paper, we performed the study using a set of subsonic airfoil optimization cases as benchmark problems. Expected hypervolume improvement was used as the acquisition function to enrich the experimental design. Results show that the choice of the covariance function give a notable impact on the performance of multi-objective BO. In this regard, Kriging models with Matern-3/2 is the most robust method in terms of the diversity and convergence to the Pareto front that can handle problems with various complexities.Natural Environment Research Council (NERC

Rewiring strategies for changing environments

A typical pervasive application executes in a changing environment: people, computing resources, software services and network connections come and go continuously. A robust pervasive application needs adapt to this changing context as long as there is an appropriate rewiring strategy that guarantees correct behavior. We combine the MERODE modeling methodology with the ReWiRe framework for creating interactive pervasive applications that can cope with changing environments. The core of our approach is a consistent environment model, which is essential to create (re)configurable context-aware pervasive applications. We aggregate different ontologies that provide the required semantics to describe almost any target environment. We present a case study that shows a interactive pervasive application for media access that incorporates parental control on media content and can migrate between devices. The application builds upon models of the run-time environment represented as system states for dedicated rewiring strategies

Compressing images by encoding their latent representations with relative entropy coding

Variational Autoencoders (VAEs) have seen widespread use in learned image compression. They are used to learn expressive latent representations on which downstream compression methods can operate with high efficiency. Recently proposed 'bits-back' methods can indirectly encode the latent representation of images with codelength close to the relative entropy between the latent posterior and the prior. However, due to the underlying algorithm, these methods can only be used for lossless compression, and they only achieve their nominal efficiency when compressing multiple images simultaneously; they are inefficient for compressing single images. As an alternative, we propose a novel method, Relative Entropy Coding (REC), that can directly encode the latent representation with codelength close to the relative entropy for single images, supported by our empirical results obtained on the Cifar10, ImageNet32 and Kodak datasets. Moreover, unlike previous bits-back methods, REC is immediately applicable to lossy compression, where it is competitive with the state-of-the-art on the Kodak dataset

Minimal random code learning: Getting bits back from compressed model parameters

While deep neural networks are a highly successful model class, their large memory footprint puts considerable strain on energy consumption, communication bandwidth, and storage requirements. Consequently, model size reduction has become an utmost goal in deep learning. A typical approach is to train a set of deterministic weights, while applying certain techniques such as pruning and quantization, in order that the empirical weight distribution becomes amenable to Shannon-style coding schemes. However, as shown in this paper, relaxing weight determinism and using a full variational distribution over weights allows for more efficient coding schemes and consequently higher compression rates. In particular, following the classical bits-back argument, we encode the network weights using a random sample, requiring only a number of bits corresponding to the Kullback-Leibler divergence between the sampled variational distribution and the encoding distribution. By imposing a constraint on the Kullback-Leibler divergence, we are able to explicitly control the compression rate, while optimizing the expected loss on the training set. The employed encoding scheme can be shown to be close to the optimal information-theoretical lower bound, with respect to the employed variational family. Our method sets new state-of-the-art in neural network compression, as it strictly dominates previous approaches in a Pareto sense: On the benchmarks LeNet-5/MNIST and VGG-16/CIFAR-10, our approach yields the best test performance for a fixed memory budget, and vice versa, it achieves the highest compression rates for a fixed test performance